In this section we describe variables related to orderflow of small traders, where, the abnormal orderflow of small traders proxies for investor sentiment for that IPO. We use trade size to classify traders into small traders. Previous literature suggests that this classification maps quite well to that of trading by individuals. Lee (1992) reports survey-based evidence that most of the transactions by individuals are of small dollar value He also argues that while large traders may break their orders into medium size, for a variety of reasons they do not trade in very small lots. Lee and Radhakrishna (2000) compare the size-based classification of investors to the actual identities obtained from the TORQ database where the identity of the traders are clearly identified, and find that trade size does a good job of separating individuals trades from trades by institutions. Not surprisingly, a large number of papers have used trade size as a proxy for small versus large investors (see, for example, Battalio and Mendenhall, 2005; Bhattacharya, 2001; and Chakravarty, 2001).
22 Admittedly, the use of trade size may not provide as clean an evidence on the trading behavior of individuals as that documented from the detailed datasets used in some prior studies (for example, Odean, 1998; and Grinblatt and Keloharju, 2001 use the exact identity of the investors). However, such detailed datasets cover only limited time periods of two or three years. The use of the well- accepted trade size proxy allows us to examine the influence of sentiment of small investors over a longer time period of 1994-2008. This measure of investor sentiment is similar in spirit to the proxy for investor sentiment in Derrien (2005) i.e., the fraction of the IPO issued to retail investors, and to the proxy for investor sentiment in Cornelli, Goldreich and Ljungqvist (2006), and Dorn (2009) i.e.,
‘grey market’ pre IPO trading. These authors argue, as we do, that investor sentiment impacts prices through trading by noise traders, who are usually thought to be retail investors (for example, Kumar and Lee, 2006).
We use the Trade and Quotation (TAQ) dataset which contains information about each executed trade for each stock. When the dollar amount of a trade is less than or equal to $5,000, we assume the trade is executed by a small investor and is consistent with the prior literature (Bhattacharya, 2001). Defining small trades using such a low cutoff allows us to minimize the impact of large traders splitting their trades into small lots and being classified as small investors.
However, since the dollar trade size would be large for high-priced stocks even for small trade lots, we follow Asthana et al. (2004) and modify the above classification for stocks whose prices exceed $50. For these stocks, we classify trades below 100 shares as trades by small investors. To ensure that our results are
23 not driven by stock price movements around the event date, the dollar values of all trades associated with an IPO are calculated by using the average of the daily share prices during the third month after the IPO.
After identifying trades executed by small investors, we follow the methodology developed by Lee and Ready (1991) to classify each trade as either buyer-initiated (i.e., a buy) or seller-initiated (i.e., a sell). The Lee-Ready algorithm matches a trade’s execution price to the most recent quote. If the trade’s execution price is above (below) the midpoint of the bid-ask spread, it is classified as a buy (sell). In case where the trade execution price is at the mid point of the bid-ask spread, the trade is classified based on a “tick-test”. An up-tick classifies a trade as a buy and a down-tick as a sell. We only consider the trades executed between 9:30am and 4:00pm, since the exact time of execution and quotes become less reliable outside of the normal market hours.
We define order flow, NetBuy, as the difference between the number of shares bought and sold.10
−
= ( )
)
, (
, NETBUY
NETBUY NETBUY
ANETBUY
i
i t i t
i σ
à
We then follow Asthana et al. (2004) and define the abnormal order flow of small investors for IPO i on event date t which is the first trading date after the IPO date as ANetBuyi,t that is computed as follows.
(8)
where ài and σi are the mean and standard deviation, respectively, of the daily order flow of the investor group for the IPO during the estimation period.
The estimation period ranges from day +30 to day +60 relative to the event date.
10 Our results remain robust if we measure order flow in terms of dollar volume of shares traded instead of number of shares traded.
24 Since there is no “grey market” in the US, and hence ex-ante retail trading and prices of IPOs are unobservable, we have no option but to use ex-post data to proxy for investor sentiment that previous literature has used. Thus there is a look ahead bias in the measurement of the trading based sentiment variable. Note that ANetBuyi,t is not our main variable of interest, but rather control variable for the firm-specific sentiment empirically examined in several related studies in European IPO samples. Hence, we feel it is justified to use it in our context; i.e. to control for previous findings.
Another possible concern is that in recent years, practice of splitting orders has become common. Specifically, large orders from institutions are split into small orders. Our algorithm to identify small traders based on trade size may result in misclassification of large traders as small traders and introduce noise in the measurement of small trader sentiment However, this will bias the results towards the null hypothesis; i.e. it will work against finding significant results.